Ontario Water Consortium - WIG Project Highlight: Using machine learning to make flood forecasts less wishy-washy

Studies show that machine-learning models are extremely good at predicting stream flow discharge. They tend to perform better and run faster than physical models. On top of that, you don’t need to train machine-learning models using data from a specific watershed — just data from a sufficiently large number of other watersheds.
— Ontario Water Consortium

An AI-produced visual displaying a 3D river model accompanied by a flood forecasting system powered by machine learning algorithms.

Click here to read the article on the Ontario Water Consortium

The Ontario Water Consortium has written an excellent article which reviews Aquanty’s latest technology driven initiative that can be used to manage water resources. With support from the Ontario Water Consortium’s Water Industry Growth Program, Aquanty is making machine-learning (i.e. artificial intelligence) driven real-time flood forecasting a reality. As climate change increases the frequency and intensity of extreme weather, flood forecasting is becoming increasingly important. But creating accurate predictions has never been easy, and shifting precipitation patterns only add to the challenge. Now, AI offers new ways to address those challenges. With a grant from OWC’s Water Industry Growth (WIG) program — which funds the development of new water technologies by Ontario companies — Aquanty has developed a new machine-learning model that can produce highly accurate predictions hours or even months into the future (forecast skill may vary). We are currently working on integrating these new capabilities into our suite of web-based water decision support tools. As one of the only companies in Canada currently doing this kind of forecasting, we are well positioned to meet a growing need for advanced forecasting tools.

ML-based streamflow forecasts are currently available for a small selection of watersheds in southwestern Ontario through Aquanty’s HGSRT web platform.

Visit www.hgsrt.com today to start a trial and request early-access to the ML-based streamflow forecasting beta testing program.

Click here to read the article on the Ontario Water Consortium

What we’re working towards essentially is putting the pieces in place so that we can combine the best strengths of both the physics-based approach to modeling and the machine-learning-based approach to forecasting stream flows,
— Brayden McNeill, Aquanty Sales and Marketing Lead
Previous
Previous

HGS RESEARCH HIGHLIGHT – Comparing alternative conceptual models for tile drains and soil heterogeneity for the simulation of tile drainage in agricultural catchments

Next
Next

HGS RESEARCH HIGHLIGHT – The coastal aquifer recovery subject to storm surge: Effects of connected heterogeneity, physical barrier and surge frequency